DocumentCode :
3259312
Title :
Extraction and analysis of digital images feature of three kinds of wheat diseases
Author :
Jinghui Li ; Lingwang Gao ; Zuorui Shen
Author_Institution :
IPMist Lab., China Agric. Univ., Beijing, China
Volume :
6
fYear :
2010
fDate :
16-18 Oct. 2010
Firstpage :
2543
Lastpage :
2548
Abstract :
In this paper, the method of automatic identification of three wheat diseases was applied by analyzing the morphological characteristics extracted from their images. The target area was got by segmenting three kinds of wheat diseases images on wheat powdery mildew, wheat sharp eyespot, and wheat stripe rust. Via extracting and optimizing the morphological data and using statistical analysis software to analysis the data with the principal component analysis and the discriminant analysis, five characteristic parameters such as Sphericity, Roundness, Hu1, Hu2, equivalent radius were selected as the identification factors. The recognizable rates of the samples among the three wheat diseases were 96.7%, 93.3%, and 86.7% respectively using the factors.
Keywords :
agricultural products; agricultural safety; feature extraction; principal component analysis; Hu1 parameter; Hu2 parameter; discriminant analysis; image feature analysis; image feature extraction; principal component analysis; roundness parameter; sphericity parameter; statistical analysis; wheat diseases; wheat powdery mildew; wheat sharp eyespot; wheat stripe rust; Computer vision; Data mining; Diseases; Feature extraction; Image color analysis; Lesions; Principal component analysis; disdcriminant analysis; morphological feature; principal component analysis; segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
Type :
conf
DOI :
10.1109/CISP.2010.5646912
Filename :
5646912
Link To Document :
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